At the very beginning of a typical magnetic resonance (MR) scan, magnetization is lined up in the direction of the main magnetic field. When the MR scan begins, one or more radiofrequency (RF) pulses may be applied to the magnetization. These pulses tip the magnetization away from the direction of the main magnetic field. If no more RF pulses occur, the magnetization begins to recover back toward is initial value. The rate at which the magnetization recovers is referred to as its “T1 value”. T1 is a characteristic property of tissue. It may be altered in the presence of pathology.
In conventional MRI, scans are typically performed that emphasize the differences in T1 between tissues and pathology. This is typically accomplished as follows: an initial RF pulse is applied; following this pulse, a delay period then occurs; during this period, the magnetization recovers back toward its equilibrium value. However, different tissues and pathology will in general have difference T1 values. Therefore, after the delay period, magnetization from different tissues/pathology will in general have recovered to different levels. In conventional MRI, a single image is typically acquired after the delay period. This produces an image that is referred to as a “T1-weighted” image, which may provide T1 information in a qualitative manner. In a T1-mapping scan, multiple images at different delay times are acquired. At each delay period, the magnetization will have recovered to a different level. In this manner, one can follow the magnetization as it recovers to its equilibrium value. To determine a tissue's T1 value, a mathematical model is fit to this recovery curve. One of the parameters of the model is the tissue T1 value. Note that this fitting procedure is performed on every pixel in the image. Therefore, the T1 value in each pixel may be determined. If these T1 value are displayed in an image format, the resulting image is referred to as a “T1 map”, which may provide T1 information in a quantitative manner.
Quantitative T1 mapping has shown promise for early identification and discrimination of pathology in a wide range of cardiac diseases. The success of these techniques may be dependent on an accurate, precise, and clinically practical cardiac T1 mapping technique. One T1 mapping technique in particular, Modified Look-Locker Inversion Recovery (MOLLI), has attracted much recent attention [1,2]. A typical MOLLI acquisition is illustrated in FIG. 1.
The basic MOLLI sequence of FIG. 1 begins with a 180° inversion pulse triggered by the R-wave of the cardiac cycle. A steady-state free precession (SSFP) readout is then performed to acquire the first inversion time (TI1). The SSFP readout may then be acquired on subsequent cardiac cycles to acquire additional inversion times (TI2). This forms the first inversion grouping. Typically, a maximum of five inversion times may be acquired before the magnetization reaches its steady state. In theory, a T1 map could be calculated from these inversion times alone [3]. However, in the interest of improving the precision of the fit, additional data is typically acquired. Therefore, the entire process, beginning with another inversion pulse, is repeated to collect TI3, TI4, TI5. If appropriate, this process may be repeated as necessary to acquire additional inversion groupings. This forms the second inversion grouping. The separately-acquired data from all inversion groupings is subsequently combined. Curve fitting is then performed on the combined data set to calculate T1. Note that in this particular example, two inversion times were acquired in the first group, and three in the second. However, this is just illustrated for example. In general, the distribution of inversion times within each inversion group may be selected to be any suitable combination.
However, the data combination presents a problem: unless the magnetization fully recovers to its equilibrium value at the end of each inversion grouping, the initial magnetization in subsequent inversion groupings will in general be different. In turn, this will lead to discontinuities in the combined data, and consequently errors in the T1 curve fit. To address this issue, conventional MOLLI techniques use an additional “rest period” during which the magnetization is allowed to recover back to its equilibrium value. (In the present disclosure, the acquisitions will be labeled as “w(x)y”, where the non-bracketed numbers indicate an inversion grouping, and the bracketed numbers indicate a rest period, indicated as a count of heartbeats.) Unfortunately, these rest periods can significantly reduce the efficiency and/or lengthen the scan time—a three heartbeat rest period is typical. Furthermore, if the rest period is not long enough, systematic errors in the curve fit may result. This may be a particular problem in patients with faster heart rates. There may be additional limitations or disadvantages in the conventional approach. For example, if an arrhythmia occurs during the rest period, this could artificially shorten the recovery period (since the rest period is based on the number of heartbeats), and thus may lead to incomplete magnetization recovery. Further, if a free-breathing navigator scan is desired, the added time required for the rest period may make the scans impractically long. Using a fixed-rate rest period, rather than one based on heart beats may reduce or minimize some of the issues associated with heart-rate sensitivity. However, such an approach may still incur a significant efficiency penalty.
Another issue associated with conventional MOLLI is that the calculated T1 value may exhibit a bias relative to the true T1 value. In conventional MOLLI, this is typically partially (but not completely) removed with a correction factor [1]. The effectiveness of this correction factor may be compromised in the absence of complete magnetization recovery between inversion groupings.
An attempt at reducing the requirement for the MOLLI rest period is the ShMOLLI technique [3,4]. Like conventional MOLLI, ShMOLLI still requires full magnetization recovery for fitting. To ensure this occurs, a conditional fitting algorithm is employed which selectively removes inversion times that did not start from the full equilibrium magnetization. There are a number of disadvantages with this approach: first, for tissues with longer T1s, there are potentially only a limited number of data points available for fitting—typically a single inversion grouping with a maximum of ˜5 points. This limits the precision of the fits. The variable number of fitted points could also lead to added variability in the precision of the fits across tissue types/pathology with different T1 values. Second, ShMOLLI typically does not eliminate the rest period, but rather reduces it to one heart beat. Third, typical ShMOLLI implementations to date have used exactly the same inversion grouping for data acquisition. While it may be possible to employ a ShMOLLI approach with other inversion groupings, the algorithm would likely have to be re-tuned and possibly re-validated for each specific case.